lewtun

# This is a modified version of TRL's `SFTTrainer` example (https://github.com/huggingface/trl/blob/main/examples/scripts/sft_trainer.py), 
# adapted to run with DeepSpeed ZeRO-3 and Mistral-7B-V1.0. The settings below were run on 1 node of 8 x A100 (80GB) GPUs.
#
# Usage:
#   - Install the latest transformers & accelerate versions: `pip install -U transformers accelerate`
#   - Install deepspeed: `pip install deepspeed==0.9.5`
#   - Install TRL from main: pip install git+https://github.com/huggingface/trl.git
#   - Clone the repo: git clone github.com/huggingface/trl.git
#   - Copy this Gist into trl/examples/scripts
#   - Run from root of trl repo with: accelerate launch --config_file=examples/accelerate_configs/deepspeed_zero3.yaml --gradient_accumulation_steps 8 examples/scripts/sft_trainer.py  

seddonm1

On an Orin NX 16G the memory was too low to compile and the SWAP file had to be increased.

/etc/systemd/nvzramconfig.sh
change:

```
# Calculate memory to use for zram (1/2 of ram)
totalmem=`LC_ALL=C free | grep -e "^Mem:" | sed -e 's/^Mem: *//' -e 's/  *.*//'`
mem=$((("${totalmem}" / 2 / "${NRDEVICES}") * 1024))
```

sorny

X11 forwarding on macOS and docker

A quick guide on how to setup X11 forwarding on macOS when using docker containers requiring a DISPLAY. Works on both Intel and M1 macs!

This guide was tested on:

• macOS Catalina 10.15.4
• docker desktop 2.2.0.5 (43884) - stable release
• XQuartz 2.7.11 (xorg-server 1.18.4)
• Macbook Pro (Intel)

mblondel

# Authors: Mathieu Blondel, Vlad Niculae
# License: BSD 3 clause

import numpy as np


def _gen_pairs(gen, max_iter, max_inner, random_state, verbose):
    rng = np.random.RandomState(random_state)

    # if tuple, interpret as randn

JerryLokjianming

---

How to Crack Sublime Text 3.2.2 Build 3211 with Hex Editor (Windows | Without License) ↓

1. Download & Install Sublime Text 3.2.2 Build 3211
2. Visit https://hexed.it/
3. Open file select sublime_text.exe
4. Offset 0x8545: Original 84 -> 85
5. Offset 0x08FF19: Original 75 -> EB
6. Offset 0x1932C7: Original 75 -> 74 (remove UNREGISTERED in title bar, so no need to use a license)

vellamike

Building TensorFlow from source for Jetson TX2 with Jetpack 3.2

Jetpack 3.2 includes Cuda 9 and CuDNN 7 so it is necessary to compile it from source.

Step 1 - Get Java

sudo apt-get install openjdk-8-jdk

Step 2 - Get some dependencies

cprakashagr

## Sublime Text 3 Serial key build is 3103

—– BEGIN LICENSE —–
Michael Barnes
Single User License
EA7E-821385
8A353C41 872A0D5C DF9B2950 AFF6F667
C458EA6D 8EA3C286 98D1D650 131A97AB
AA919AEC EF20E143 B361B1E7 4C8B7F04
B085E65E 2F5F5360 8489D422 FB8FC1AA

peterjc123

# Prerequisites
# 1. MSVC 2017 C++ Build Tools
# 2. CMAKE 3.0 or up
# 3. 64 bits of Windows
# 4. Anaconda / MiniConda 64 bits

# Prerequisites for CUDA
# 1. CUDA 8.0 or up
# 2. NVTX( in CUDA as Visual Studio Integration. if fail to install, you can extract
#     the CUDA installer exe and found the NVTX installer under the CUDAVisualStudioIntegration)

jkjung-avt

# --------------------------------------------------------
# Camera sample code for Tegra X2/X1
#
# This program could capture and display video from
# IP CAM, USB webcam, or the Tegra onboard camera.
# Refer to the following blog post for how to set up
# and run the code:
#   https://jkjung-avt.github.io/tx2-camera-with-python/
#
# Written by JK Jung <jkjung13@gmail.com>

jganzabal

This configuration worked for me, hope it helps

It is based on: https://becominghuman.ai/deep-learning-gaming-build-with-nvidia-titan-xp-and-macbook-pro-with-thunderbolt2-5ceee7167f8b

and on: https://stackoverflow.com/questions/44744737/tensorflow-mac-os-gpu-support

Hardware